Research / Sources
AI research and discussion on LessWrong. www.lesswrong.com
Argues that autonomous AI researchers will overwhelm peer review, funding and human understanding of AI safety science, and proposes a metascience organisation to address these failures.
A distillation of the AI2040 alignment roadmap, laying out a Plan C strategy for a responsible frontier lab: phases of mitigation, pause, handoff to AIs at a Min-H capability threshold, and alternative exit plans.
A rough strategic essay ranking which technical AI safety research fields are most likely to be automated by AI, based on feedback quality and economic incentives for frontier labs.
Argues that alignment research should be converted into hill-climbable tasks that AI models can automate, and discusses reward hacking risks and mitigations such as held-out validation sets and honeypots.
Sketches a realistic target future where superintelligent AI stays under permanent human control through dynamic alignment targets, structured multi-source feedback, and institutional norms, without claiming a technical solution.
Argues that superalignment, using AI labour to solve alignment during a slow-takeoff window, is unlikely to work because its necessary conditions are unlikely to hold and the plan may cause harm.
Proposes concrete projects, such as compiled monofiles, indexable docs, package guides, sandboxes and benchmarks, to help coding agents automate technical AI safety research, mainly mechanistic interpretability.
Argues that AI agents short of takeover-level capability could speed up safety research 3-10x, using a parameter-decomposition interpretability project as the example, and that safety researchers should adopt AI tooling.
Essay arguing that safe superintelligence may require decades of philosophical progress, possibly accelerated by human-AI collaboration, and that loss of human control is the central risk on the current path.
Argues that AI can safely automate alignment research if agents are trained for truth-seeking, with humans nudging rather than grading ground truth, provided agents stay non-egregiously misaligned.